Image Generation

Generate images from text prompts with Venice’s native image API or the OpenAI-compatible images endpoint, with style controls and binary or base64 output.

Image generation on Venice is synchronous. Send a prompt to /image/generate and receive your image in the same response, either as base64 inside JSON or as raw binary when return_binary is true.

Endpoints

Endpoint	Purpose	When to use
POST /image/generate	Native Venice image generation API	Use this for full feature support
GET /image/styles	List available style presets	Use this before sending style_preset
POST /images/generations	OpenAI-compatible image generation API	Use this when migrating existing OpenAI image clients

Step 1: Send a generation request

Sizing is model-specific. Some models accept explicit width and height; some expose aspect_ratio; and resolution-tier models expose aspect_ratio plus resolution values such as 1K, 2K, or 4K.

Pixel-based sizing example:

POST https://api.venice.ai/api/v1/image/generate
Authorization: Bearer $VENICE_API_KEY
Content-Type: application/json

{
  "model": "venice-sd35",
  "prompt": "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour, warm window lights reflecting on the water",
  "negative_prompt": "blurry, low quality, distorted anatomy, text, watermark",
  "width": 1024,
  "height": 1024,
  "format": "webp"
}

Aspect-ratio sizing example:

POST https://api.venice.ai/api/v1/image/generate
Authorization: Bearer $VENICE_API_KEY
Content-Type: application/json

{
  "model": "qwen-image-2",
  "prompt": "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour",
  "aspect_ratio": "16:9",
  "format": "webp"
}

Resolution-tier sizing example:

POST https://api.venice.ai/api/v1/image/generate
Authorization: Bearer $VENICE_API_KEY
Content-Type: application/json

{
  "model": "gpt-image-2",
  "prompt": "A cinematic wide shot of a gondola passing through a narrow Venice canal at blue hour",
  "aspect_ratio": "16:9",
  "resolution": "4K",
  "format": "png"
}

The same pattern applies to other resolution-tier models:

{
  "model": "nano-banana-pro",
  "prompt": "A serene canal in Venice at sunset",
  "aspect_ratio": "16:9",
  "resolution": "2K"
}

Use Image Models or the Models API to confirm which sizing fields each model accepts.

Response (200):

{
  "id": "generate-image-1234567890",
  "images": [
    "UklGRiIAAABXRUJQVlA4IBYAAAAwAQCdASoQABAAPm..."
  ],
  "timing": {
    "inferenceDuration": 1840,
    "inferencePreprocessingTime": 22,
    "inferenceQueueTime": 31,
    "total": 1893
  }
}

The images array contains base64-encoded image data. Decode the first item to save or display it. timing.total is the full request duration in milliseconds.

Step 2: Decode and save the image

Python

import base64
import os
import requests

response = requests.post(
    "https://api.venice.ai/api/v1/image/generate",
    headers={
        "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
        "Content-Type": "application/json",
    },
    json={
        "model": "venice-sd35",
        "prompt": "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour, warm window lights reflecting on the water",
        "width": 1024,
        "height": 1024,
        "format": "webp",
    },
)

data = response.json()
image_bytes = base64.b64decode(data["images"][0])

with open("output.webp", "wb") as f:
    f.write(image_bytes)

print(f"Saved image from request {data['id']}")

import fs from "fs";

const response = await fetch("https://api.venice.ai/api/v1/image/generate", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "venice-sd35",
    prompt: "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour, warm window lights reflecting on the water",
    width: 1024,
    height: 1024,
    format: "webp",
  }),
});

const data = await response.json();
const imageBuffer = Buffer.from(data.images[0], "base64");
fs.writeFileSync("output.webp", imageBuffer);

console.log(`Saved image from request ${data.id}`);

Step 3: Return binary instead of JSON (optional)

If you want the response body to be the image file itself, set return_binary: true. This is useful when you want to stream or save the image directly without base64 decoding.

curl https://api.venice.ai/api/v1/image/generate \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -o output.png \
  -d '{
    "model": "qwen-image-2",
    "prompt": "Minimalist poster of a moonlit Venetian bridge in deep blue tones",
    "format": "png",
    "return_binary": true
  }'

When return_binary is true, the response body is raw image/jpeg, image/png, or image/webp data based on the format you requested.

variants is only supported when return_binary is false.

---

Step 4: List available image styles (optional)

If you want to use style_preset, first fetch the available styles from /image/styles:

curl https://api.venice.ai/api/v1/image/styles \
  -H "Authorization: Bearer $VENICE_API_KEY"

Response (200):

[
  "3D Model",
  "Analog Film",
  "Anime",
  "Cinematic",
  "Digital Art"
]

Then pass one of those values into your generation request:

{
  "model": "qwen-image-2",
  "prompt": "A futuristic Venice skyline at sunrise",
  "style_preset": "Cinematic"
}

Use the styles endpoint when you want exact preset names instead of guessing them.

---

Request Parameters

Parameter	Type	Required	Default	Description
model	string	Yes	-	Model ID to use for generation
prompt	string	Yes	-	What to generate
negative_prompt	string	No	-	What to avoid in the image
width	integer	No	1024	Output width in pixels for pixel-based models such as venice-sd35 and qwen-image
height	integer	No	1024	Output height in pixels for pixel-based models such as venice-sd35 and qwen-image
format	string	No	webp	Output format: jpeg, png, or webp
variants	integer	No	1	Number of images to generate (1-4), only when return_binary is false
return_binary	boolean	No	false	Return raw image bytes instead of base64 JSON
safe_mode	boolean	No	true	Blur adult content when enabled
seed	integer	No	random	Reuse the same seed for more consistent iterations
cfg_scale	number	No	model-dependent	Higher values push the model to follow the prompt more closely
style_preset	string	No	-	Apply a preset style from Image Styles
aspect_ratio	string	Conditional	-	Used by models that support ratio-based sizing, such as qwen-image-2, gpt-image-2, nano-banana-2, and nano-banana-pro
resolution	string	Conditional	-	Used by models that support resolution tiers such as 1K, 2K, or 4K
enable_web_search	boolean	Conditional	false	Allows supported models to use current web information; adds extra cost

Validation is model-specific. Check Image Models and the Models API before relying on a parameter across multiple models.

---

Model-specific options

High-resolution generation

Some image models support aspect_ratio without a selectable resolution tier. For example, qwen-image-2 accepts aspect ratio and maps it to model-specific output dimensions:

{
  "model": "qwen-image-2",
  "prompt": "Editorial product photo of a luxury watch on black marble, dramatic studio lighting",
  "aspect_ratio": "16:9"
}

Other image models support aspect_ratio plus a resolution tier. For example, gpt-image-2, nano-banana-2, and nano-banana-pro support 1K, 2K, and 4K:

{
  "model": "gpt-image-2",
  "prompt": "Editorial product photo of a luxury watch on black marble, dramatic studio lighting",
  "aspect_ratio": "16:9",
  "resolution": "4K"
}

{
  "model": "nano-banana-2",
  "prompt": "Editorial product photo of a luxury watch on black marble, dramatic studio lighting",
  "aspect_ratio": "16:9",
  "resolution": "2K"
}

Use Image Models to see which models support higher resolutions and how they are priced.

Adult content and safe mode

safe_mode controls whether adult content in generation output is blurred. It defaults to true. Set it to false if your use case allows adult content and you want the raw output:

{
  "model": "lustify-v8",
  "prompt": "…",
  "safe_mode": false
}

Some image models are specifically designed for adult / uncensored generation and pair well with safe_mode: false. Examples on the pricing page include lustify-sdxl, lustify-v7, and lustify-v8. See Image Models and Pricing for the current list and per-model costs.

For editing existing images, the default edit model qwen-edit blocks explicit sexual imagery. If you need uncensored editing, use qwen-edit-uncensored via Image Editing.

When safe_mode is enabled and the model returns blurred or moderated output, inspect the x-venice-is-blurred and x-venice-is-content-violation response headers if you need to detect that programmatically.

Style presets

If the selected model supports it, style_preset lets you steer the output without rewriting your whole prompt. You can fetch valid preset names from Image Styles:

{
  "model": "qwen-image-2",
  "prompt": "A futuristic Venice skyline at sunrise",
  "style_preset": "3D Model"
}

See Image Styles for the current style list.

---

OpenAI-compatible endpoint

If you’re already using OpenAI image SDKs or existing DALL-E integrations, Venice also supports POST /images/generations. It offers a simpler request format, but fewer features than the native Venice endpoint.

Request:

{
  "model": "qwen-image-2",
  "prompt": "A clean isometric illustration of an AI control room",
  "size": "1024x1024",
  "response_format": "b64_json"
}

Use the OpenAI-compatible route for faster migrations. Use /image/generate when you need Venice-specific options such as cfg_scale, style_preset, variants, or binary responses.

---

Prompting tips

1. Start with the subject, then add medium, lighting, composition, and mood.
2. Put must-avoid details in negative_prompt instead of overloading the main prompt.
3. Reuse seed when iterating so you can compare prompt changes without fully changing the composition.
4. Keep sizing model-aware. Some models use width/height, some use aspect_ratio, and resolution-tier models use aspect_ratio plus resolution.
5. Use variants during exploration, then switch back to a single output once you’ve locked in the direction.

---

Errors

Status	Meaning	Action
400	Invalid request parameters	Check field names, types, and model-specific constraints
401	Authentication failed or model requires a higher access tier	Check your API key and model access
402	Insufficient balance	Add credits at venice.ai/settings/api
415	Invalid content type	Send JSON with Content-Type: application/json
429	Rate limit exceeded or model overloaded	Retry with backoff; check Retry-After header
500	Inference processing failed	Retry the request
503	Model at capacity	Retry after a short delay

When Safe Venice is enabled, inspect response headers such as x-venice-is-blurred and x-venice-is-content-violation if you need to detect moderation outcomes programmatically.

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Available Models

See Image Models for the current model list, pricing, and feature support.